Optimizing Supply Chain Traceability: A Hybrid MCDM Approach (BWM–ANP) for Blockchain Platform Selection in Pharmaceutical and Healthcare Systems

2025-11-21T08:32:11+0330

The pharmaceutical and healthcare supply chain faces growing risks, including falsified or substandard drugs, complex global sourcing, and stringent data protection regulations. Although digital track-and-trace tools, mobile authentication, and data analytics offer potential solutions, many current systems remain isolated and vulnerable to manipulation. Blockchain and other distributed ledger technologies provide decentralized, tamper-evident infrastructures suitable for drug traceability, yet selecting an appropriate platform is a complex and stakeholder-dependent decision.

This study proposes a hybrid multi-criteria decision-making framework that integrates the Best–Worst Method (BWM) with the Analytic Network Process (ANP) to guide platform selection for pharmaceutical traceability. Criteria are organized into five groups: technical performance, privacy and security, traceability and anti-counterfeiting quality, regulatory and compliance requirements, and organizational-economic-ecosystem factors. Expert evaluations from operations, regulatory, IT/blockchain, and strategic roles are first synthesized through BWM to derive consistent criterion weights, while ANP captures interdependencies among criteria through a weighted supermatrix. The framework is applied to four platform configurations: a Fabric-based consortium blockchain, an enterprise Ethereum network (Besu/Quorum), a sector-specific traceability platform inspired by PharmaLedger, and a public Ethereum plus Layer-2 setup. Results indicate that the sector-specific consortium solution holds the highest overall priority, emphasizing the importance of regulatory fit, identity management, and tamper-evident traceability in platform selection.

An optimization Approach for Supply Chain Network Design under Uncertainty

2025-12-18T18:31:18+0330

This paper presents an optimization framework for supply chain network design (SCND) under uncertainty that integrates stochastic, robust, and distributionally robust methods with modern data-driven estimation to improve cost-effectiveness, resilience, and sustainability. The proposed approach models facility location, capacity, inventory and transportation decisions in a two-stage mixed-integer program, accommodates multiple uncertainty representations (scenarios, ambiguity sets, parameter distributions), and applies computational decomposition and sampling-based solution techniques. Numerical experiments with case studies demonstrate that hybrid stochastic–robust and distributionally robust formulations provide superior out-of-sample performance compared to purely deterministic or single-method formulations, particularly under limited data and disruption-prone environments. The findings highlight trade-offs among cost, robustness, and service levels and point to future research directions: integration with real-time data, multi-criteria sustainability objectives, and scalable solution algorithms.

International journal of industrial engineering and operational research

Optimizing Supply Chain Traceability: A Hybrid MCDM Approach (BWM–ANP) for Blockchain Platform Selection in Pharmaceutical and Healthcare Systems

An optimization Approach for Supply Chain Network Design under Uncertainty